CycleSR: unsupervised learning for 3D fingerprint super-resolution

Yongbo Yao; Haixia Wang; Yilong Zhang

doi:10.1117/12.3023157

15 February 2024 CycleSR: unsupervised learning for 3D fingerprint super-resolution

Yongbo Yao, Haixia Wang, Yilong Zhang

Proceedings Volume 13069, International Conference on Optical and Photonic Engineering (icOPEN 2023); 130690D (2024) https://doi.org/10.1117/12.3023157
Event: International Conference on Optical and Photonic Engineering (icOPEN 2023), 2023, Singapore, Singapore

Abstract

Fringe projection profilometry has been applied to measure 3D information of fingertip and collect contactless 3D fingerprints. When low-resolution (LR) camera is used in the system due to reasons such as cost, the captured fringe patterns may appear blurry, which results in less obvious contrast between valleys and ridges in the reconstructed contactless 3D fingerprints. To address this issue, we introduce an unsupervised super-resolution (SR) method that solely relies on low-resolution fringe patterns. Our approach combines a two-loop generative adversarial network. In the forward loop, a binarized interpolation loss function is designed to ensure that the upsampling generator preserves ridge and valley details. In the backward loop, the discriminator ensures that the fringe patterns produced by the downsampling generator are both repeatable and similar to the original fringe patterns. Finally, the fringe patterns are reconstructed to obtain 3D fingerprints. Experimental results demonstrate the advantages of our proposed method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongbo Yao, Haixia Wang, and Yilong Zhang "CycleSR: unsupervised learning for 3D fingerprint super-resolution", Proc. SPIE 13069, International Conference on Optical and Photonic Engineering (icOPEN 2023), 130690D (15 February 2024); https://doi.org/10.1117/12.3023157

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